During a viral outbreak, such as COVID-19, autonomously operated robots are in high demand. Robots effectively improve the environmental concerns of contaminated surfaces in public spaces, such as airports, public transport areas and hospitals, that are considered high-risk areas. Indoor spaces walls made up most of the indoor areas in these public spaces and can be easily contaminated. Wall cleaning and disinfection processes are therefore critical for managing and mitigating the spread of viruses. Consequently, wall cleaning robots are preferred to address the demands. A wall cleaning robot needs to maintain a close and consistent distance away from a given wall during cleaning and disinfection processes. In this paper, a reconfigurable wall cleaning robot with autonomous wall following ability is proposed. The robot platform, Wasp, possess inter-reconfigurability, which enables it to be physically reconfigured into a wall-cleaning robot. The wall following ability has been implemented using a Fuzzy Logic System (FLS). The design of the robot and the FLS are presented in the paper. The platform and the FLS are tested and validated in several test cases. The experimental outcomes validate the real-world applicability of the proposed wall following method for a wall cleaning robot.

Complete area-coverage path planners are essential for robots performing tasks like cleaning, inspection, and surveying. However, they often involve complex calculations, mapping, and determining movement directions, leading to high computational or processing overheads and the risk of deadlocks. This paper proposes an approach for cleaning, i.e., by linear wiping of generic and discontinuous surfaces in hospital settings using inhouse assembled mobile dual-arm (MDA) robotic system. The proposed framework introduces key features: (a) a less resource-intensive approach for MDA positioning and cleaning surface mapping, (b) Modified Glasius Bioinspired Neural Network through use of heuristics (GBNN+H) to optimize surface linear wiping while obstacle avoidance, and traversal across discontinuous surfaces. The advantages of the proposed algorithm are highlighted in simulation with GBNN+H significantly reduces the number of steps and flight time required for complete coverage compared to existing algorithms. The proposed framework is also experimentally demonstrated in a hospital setting, paving the way for improved automation in cleaning and disinfection tasks. Overall, this work presents a generic and versatile, applicable to various surface orientations and complexities.

Complete coverage path planning (CCPP) is essential for autonomous cleaning robots, particularly in complex and variable environments where traditional, fixed-footprint designs may fall short. This paper presents adaptive Glasius bio-inspired neural network (aGBNN) approach to CCPP, specifically tailored for an omnidirectional self-reconfigurable cleaning robot (OSCR). The aGBNN method dynamically generates a complete coverage path by leveraging the ability to change sweeping footprint of the robot (SFR) assisted by reconfiguring brushes design. The sweeping is carried out both longitudinally and laterally, thereby complementing the omnidirectional locomotion with cleaning. Unlike conventional CCPP algorithms that assume a fixed robot footprint, the proposed aGBNN adapts in real-time to spatial and moving obstacles, significantly enhancing coverage efficiency. Experimental and simulation results demonstrate the advantage of the aGBNN approach, in terms of path length, and total time to complete area coverage compared to state-of-the-art methods.

Autonomous mobile robots (AMRs) face challenges in navigating complex environments efficiently. To manoeuvre through both narrow and wide spaces, AMRs require two essential design features: a compact form for tight areas as well as a large configuration with omnidirectional movement for wide spaces. This study utilizes inhouse designed Expand and Collapse Variable Width Robot (ECVWR) to demonstrate effective area coverage. These ECVWR can contract to navigate constrained spaces and expand to optimize coverage in open areas. Existing methods for achieving complete area coverage do not account for the reconfiguration or the change in footprint. To address this issue, we propose the Depth-First Search (DFS) for Complete Coverage Path Planning Strategy (CCPPS) for ECVWRs. This method allows adjustments to the generation of waypoints in CCPPS, minimizing the path length. The simulation study shows that our proposed CCPPS outperforms contemporary state-of-the-art CCPPS, namely, GBNN, exhibiting superior expanded area coverage, reduced travel distance, and enhanced computational efficiency. Moreover real-world experiment to further benchmark the efficacy of our proposed algorithm. The proposed CCPPS is generic and can be extended to other variable footprint robots.

In reconfigurable robotics, intra-reconfiguration enables a robot to change its functional abilities, while inter-reconfiguration manipulates the specification limits of the robot hardware. Although the versatility of inter-reconfigurable robots is desired in advanced autonomous systems, the O(n $^{3})$ algorithm computational time complexity challenge comes when multiple modular robots combine and reconfigure into a bigger form structure for autonomous navigation tasks. This phenomenon has limited the inter-reconfiguration potential of expansion, versatility, and robustness. In this paper, a navigation framework with non-complex transformation states is proposed for inter-reconfigurable robots to perform combining and splitting control dimensions. Simulations have shown the complexity from O(n) to constant time O(1) in the reconfiguration states of the framework on a considerable number of robot agents. Additionally, a set of inter-reconfigurable robots, Wasp Biggie, was used to demonstrate the proof-of-concept in experiments as a fully functional centralized planner system. These experiments showed outperforming results on the consistent utility of CPU consumption while performing navigation and reconfiguration. Note to Practitioners —This study aims to provide controls for combining multiple robots into a single system. The research study enables the robots in the system to vary and manipulate their physical structure and mechanism limitations. Onboard computation resources are often finite and incapable of computing for multiple functions that are actively in demand. Hence, this paper is motivated by the severe increase in computational demands common in a multi-robot centralized system. The paper has provided the technical details of the system architecture of the state machine and how it integrates with a typical navigation stack. The state-machine of the framework can be easily constructed using the SMACH package in the Robot Operating System and integrated by reconstruction of the navigation stack by providing the command velocity as an input and producing the transformed command velocity for the controls of the respective robots. The outcome of the paper provides a method of control in inter-reconfigurability and takes in a consistent amount of computational resources as the number of robots varies in utilization.